A Relaxation Approach to Dynamic Sensor Selection in Large-Scale Wireless Networks

Weimer, James; Sinopoli, Bruno; Krogh, Bruce H.

doi:10.1109/icdcs.workshops.2008.82

Cited by 35 publications

(26 citation statements)

References 17 publications

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“…By applying the property (iii) in Lemma 3 to the trace term of h(k|i, j) in (18), we observe the performance constraints in (19) can be conservatively relaxed sincê (25) with, by defining x • y to be the Schur product of x and y, (26) and P 1,k and P 2,k such that…”

Section: Methodsmentioning

confidence: 99%

Periodic constraint-based control using dynamic wireless sensor scheduling

Weimer

Araújo

Hernández

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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Constraint-based control over wireless sensor networks (WSNs) require control strategies that achieve a desired closed-loop system performance while using minimal network resources. In addition to constraints associated with distributed control, WSNs have limitations on bandwidth, energy consumption, and transmission range. This paper introduces and experimentally evaluates a new receding-horizon approach for performing constraint-based control using a WSN. By leveraging the system controllability, the receding-horizon controller is formulated as a mixed-integer programming problem which, at each time step, simultaneously generates a control sequence and sensor selection schedule such that the desired performance is achieved while minimizing the energy required to perform data acquisition and control. For systems containing many sensors, a multi-step state estimator is employed to implement the receding-horizon controller using a conservative abstractionrelaxation approach that simplifies the original mixed-integer programming problem into a convex quadratic programming problem. A wireless process control test bed consisting of 8 coupled water tanks and 16 wireless sensors are used to experimentally evaluate the receding-horizon controller.

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Section: Methodsmentioning

confidence: 99%

Periodic constraint-based control using dynamic wireless sensor scheduling

Weimer

Araújo

Hernández

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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“…This problem can be non-trivial because the function of the next-step error covariance matrix can be non-convex. Authors in [25] propose to minimize the trace of the next step error covariance matrix to find the maximum of the function. They propose a relaxation approach that finds a computationally feasible sub-optimal solution.…”

Section: A Information-driven Techniquesmentioning

confidence: 99%

On Energy Efficiency in Collaborative Target Tracking in Wireless Sensor Network: A Review

Demigha

Hidouci

Ahmed

2013

IEEE Commun. Surv. Tutorials

150

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Abstract-Energy-efficiency in target tracking applications has been extensively studied in the literature of Wireless Sensor Networks (WSN). However, there is little work which has been done to survey and summarize this effort. In this paper, we address the lack of these studies by giving an up-to-date Stateof-the-Art of the most important energy-efficient target tracking schemes. We propose a novel classification of schemes that are based on the interaction between the communication subsystem and the sensing subsystem on a single sensor node. We are interested in collaborative target tracking instead of singlenode tracking. In fact, WSNs are often of a dense nature, and redundant data that can be received from multiple sensors help at improving tracking accuracy and reducing energy consumption by using limited sensing and communication ranges. We show that energy-efficiency in a collaborative WSN-based target tracking scheme can be achieved via two classes of methods: sensing-related methods and communication-related methods. We illustrate both of them with several examples. We show also that these two classes can be related to each other via a prediction algorithm to optimize communication and sensing operations. By self-organizing the WSN in trees and/or clusters, and selecting for activation the most appropriate nodes that handle the tracking task, the tracking algorithm can reduce the energy consumption at the communication and the sensing layers. Thereby, network parameters (sampling rate, wakeup period, cluster size, tree depth, etc.) are adapted to the dynamic of the target (position, velocity, direction, etc.). In addition to this general classification, we discuss also a special classification of some protocols that put specific assumptions on the target nature and/or use a "non-standard" hardware to do sensing. At the end, we conduct a theoretic comparison between all these schemes in terms of objectives and mechanisms. Finally, we give some recommendations that help at designing a WSN-based energy efficient target tracking scheme.

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“…This is motivated by the following fact: if the means are estimated as sample means (the minimum variance unbiased estimate for Gaussian distributions), then the covariance of the estimate of the ith mean is equal to 1 N S i , where N is the number of iid samples used. Therefore, it is natural to assume that the confidence region for m i is given by (5), where the scaling constants k 0 and k 1 are proportional to the number of samples used.…”

Section: Distributionsmentioning

confidence: 99%

“…However, the MLD usesm 0 andm 1 . We want to assess the classifier performance wheñ m 0 ,m 1 drift inside the confidence regions defined by (5). To this end, we generate 3000 pairs of points (m 0 ,m 1 ) from the confidence region (5); for each of them we estimate probability of false alarm (P FA ).…”

Section: A Simulation Examplementioning

confidence: 99%

Robust linear dimensionality reduction for hypothesis testing with application to sensor selection

Bajović

Sinopoli

2009

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-This paper addresses robust linear dimensionality reduction (RLDR) for binary Gaussian hypothesis testing. The goal is to find a linear map from the high dimensional space where the data vector lives to a low dimensional space where the hypothesis test is carried out. The linear map is designed to maximize the detector performance. This translates into maximizing the Kullback-Leibler (KL) distance between the two projected distributions. In practice, the distribution parameters are estimated from training data, thus subject to uncertainty. This is modeled by allowing the distribution parameters to drift within some confidence regions. We address the case where only the mean values of the Gaussian distributions, m 0 and m 1 , are uncertain with confidence ellipsoids defined by the corresponding covariance matrices, S 0 and S 1 . Under this setup, we find the linear map that maximizes the KL distance for the worst case drift of the mean values. We solve the problem globally for the case of linear mapping to one dimension, reducing it to a grid search over a finite interval. Our solution shows superior performance compared to robust linear discriminant analysis techniques recently proposed in the literature. In addition, we use our RLDR solution as a building block to derive a sensor selection algorithm for robust event detection, in the context of sensor networks. Our sensor selection algorithm shows quasi-optimal performance: worstcase KL distance for suboptimal sensor selection is at most 15% smaller than worst-case KL distance for the optimal sensor selection obtained by exhaustive search.

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A Relaxation Approach to Dynamic Sensor Selection in Large-Scale Wireless Networks

Abstract: Wireless sensor networks (WSNs)

Cited by 35 publications

References 17 publications

Periodic constraint-based control using dynamic wireless sensor scheduling

Periodic constraint-based control using dynamic wireless sensor scheduling

On Energy Efficiency in Collaborative Target Tracking in Wireless Sensor Network: A Review

Robust linear dimensionality reduction for hypothesis testing with application to sensor selection

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